The advent of electronic commerce has led to an increasingly sophisticated array of networked financial products and services, and consumer tools to access and analyze those products and services. Online shopping comparators, in which categories of consumer goods or services are sorted by price, are known. Reverse auction services, in which a consumer names a price and then a search engine attempts to match that price amongst participating vendors, are also known. In the realm of financial products and services, a host of Internet-based banking, mutual funds, and other financial tools have been deployed.
In the case of mutual funds, the subject product involves a set of performance numbers and other quantities which require more than a simple, one-field comparison on the basis of price. As a result, shopping for networked mutual fund products typically involves running a comparison engine in which a consumer wishing to invest in a mutual fund enters a set of predefined ranges for several variables fitting their needs, and pertaining to funds in the search set. For instance, the consumer may enter a request for comparison of funds whose 5-year average return is at least 20% with an expense load of no more than 1.5%. Conventional search engines will then access some type of database whose fields correspond to these predefined variables, and return only those mutual fund products matching the complete criteria set by the inquirer.
However, those types of comparison engines suffer from more than one drawback. For one, if a candidate mutual fund lacks one of the selected criteria but very satisfactorily meets all of the remainder, conventional search engines will omit that product from the presentation of search results. Moreover, while such engines permit a user to input ranges for different criteria, once they are entered those ranges are not weightable. That is, the user is not afforded the opportunity to create a sliding scale of importance to be applied to the various quantitative factors supported by the search engine, or to sort out results once hits are found based on variable weights. In addition, conventional search engines are not equipped to allow a user to re-search an existing collection of hits by adding, deleting or adjusting one or more criteria or weights on those criteria, to refine searches and focus in on products of particular interest.
Further, conventional search engines may be constrained in the input feeds they use, and not be able to obtain multiple feeds or frequent or realtime updates. More flexible and robust financial search technology is desirable.